The Accessibility to Healthcare 2019 dataset enumerates land-based travel time (in minutes) to the nearest hospital or clinic for all areas between 85 degrees north and 60 degrees south for a nominal year 2019. It also includes “walking-only” travel time, using non-motorized means of transportation only.
Major data collection efforts underway by OpenStreetMap, Google Maps, and academic researchers have been harnessed to compile the most complete collection of healthcare facility locations to date. This map was produced through a collaboration between MAP (University of Oxford), Telethon Kids Institute (Perth, Australia), Google, and the University of Twente, Netherlands.
This project builds on previous work published by Weiss et al 2018 (doi:10.1038/nature25181) that utilised datasets for roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a “friction surface”: a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. For the current project, an updated friction surface was created to incorporate recent improvements within OSM roads data.
On the another hand, the WorldPop project at University of Southampton provides a range of world population datasets at 100 m spatial resolution. According to the providers, “statistical assessments suggest that the resulting maps are consistently more accurate than existing population map products, as well as the simple gridding of the census data”.
This R notebook gathers both the Accessibility to Healthcare 2019 dataset and the Population from GEE, and clip the datasets intersecting two transnational watersheds, the Mira-Mataje’s and the Putumayo’s. Then, it shows both the accessibility and the population. It happens thanks to Malaria Project, the WorldPop project, and the rgee library.
## ── rgee 1.0.9 ─────────────────────────────────────── earthengine-api 0.1.266 ──
## ✓ email: not_defined
## ✓ Initializing Google Earth Engine:
✓ Initializing Google Earth Engine: DONE!
##
✓ Earth Engine user: users/ializarazos
## ────────────────────────────────────────────────────────────────────────────────aoi1 <- "shp/Putumayo/Cuenca_Putumayo.shp" %>%
st_read(quiet = TRUE) %>%
sf_as_ee()
aoi2 <- "shp/Mira_Mataje/Cuencas_Mira_y_Mataje.shp" %>%
st_read(quiet = TRUE) %>%
sf_as_ee()roi = aoi1$merge(aoi2)###############################
# Asset List
###############################
dataset = ee$Image('Oxford/MAP/accessibility_to_healthcare_2019')
accessibility = dataset$select('accessibility')$clip(roi)
pop1 = ee$Image('WorldPop/GP/100m/pop_age_sex/ECU_2020')$select("population")$clip(roi)
pop2 = ee$Image('WorldPop/GP/100m/pop_age_sex/COL_2020')$select("population")$clip(roi)
pop3 = ee$Image('WorldPop/GP/100m/pop_age_sex/PER_2020')$select("population")$clip(roi)
pop4 = ee$Image('WorldPop/GP/100m/pop_age_sex/BRA_2020')$select("population")$clip(roi)pop = ee$ImageCollection$fromImages(list(pop1, pop2, pop3, pop4))$mosaic()###############################
# Initialize Map Location
###############################
Map$setCenter(-77.1, 0.8, 8) # Tulcan###############################
# Map Layers
###############################
m1 = Map$addLayer(accessibility, accessibilityVis, 'Accessibility')
m1 %>% addProviderTiles("OpenTopoMap", options = providerTileOptions(opacity = 0.7))Note that the darker the tone, the better the accessibility to healthcare (in terms of land-based time travel).
###############################
# Map Layers
###############################
m1 = Map$addLayer(pop, popVis, 'Population')
m1 %>% addProviderTiles("OpenTopoMap", options = providerTileOptions(opacity = 0.7))Note that the greenish the color, the larger the population.
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] leaflet.providers_1.9.0 leaflet_2.0.4.1 RColorBrewer_1.1-2
## [4] magick_2.7.2 sf_0.9-8 rgeeExtra_0.0.1
## [7] rgee_1.0.9
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 lattice_0.20-41 png_0.1-7 class_7.3-17
## [5] ps_1.6.0 assertthat_0.2.1 digest_0.6.27 utf8_1.2.1
## [9] V8_3.4.2 R6_2.5.0 evaluate_0.14 e1071_1.7-7
## [13] geojson_0.3.4 pillar_1.6.1 rlang_0.4.11 lazyeval_0.2.2
## [17] curl_4.3.1 rstudioapi_0.13 raster_3.4-10 jquerylib_0.1.4
## [21] geojsonio_0.9.4 Matrix_1.2-18 reticulate_1.20 rmarkdown_2.8
## [25] foreign_0.8-80 jqr_1.2.1 stringr_1.4.0 htmlwidgets_1.5.3
## [29] proxy_0.4-25 compiler_4.0.3 xfun_0.23 base64enc_0.1-3
## [33] pkgconfig_2.0.3 rgeos_0.5-5 htmltools_0.5.1.1 tidyselect_1.1.1
## [37] tibble_3.1.2 httpcode_0.3.0 codetools_0.2-16 fansi_0.5.0
## [41] crayon_1.4.1 dplyr_1.0.6 crul_1.1.0 grid_4.0.3
## [45] jsonlite_1.7.2 lifecycle_1.0.0 DBI_1.1.1 magrittr_2.0.1
## [49] units_0.7-1 KernSmooth_2.23-17 cli_2.5.0 stringi_1.6.2
## [53] sp_1.4-5 bslib_0.2.5.1 ellipsis_0.3.2 generics_0.1.0
## [57] vctrs_0.3.8 geojsonsf_2.0.1 tools_4.0.3 leafem_0.1.6
## [61] glue_1.4.2 purrr_0.3.4 crosstalk_1.1.1 processx_3.5.2
## [65] yaml_2.2.1 maptools_1.1-1 classInt_0.4-3 knitr_1.33
## [69] sass_0.4.0